--- title: NASNet canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/NASNet" md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/NASNet.md" repository: Qualcomm-AI-Hub/NASNet last_updated: 2026-10-16 license: apache-2.0 pipeline_tag: image-classification tasks: - image-classification downloads: 0 stars: 0 tags: - bu_auto - android --- # NASNet > NASNet - Qualcomm-AI-Hub 在 ModelScope 开源的模型。NASNet: Optimized for Qualcomm Devices Qualcomm-AI-Hub/NASNet 是 ModelScope 魔搭社区上的image-classification模型，采用 apache-2.0 许可。 - **Repository**: Qualcomm-AI-Hub/NASNet - **License**: apache-2.0 - **Tasks**: image-classification - **Tags**: bu_auto, android - **Downloads**: 0 - **Stars**: 0 - **Last updated**: 2026-10-16 Source: https://www.modelscope.cn/models/Qualcomm-AI-Hub/NASNet --- ![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/nasnet/web-assets/model_demo.png) # NASNet: Optimized for Qualcomm Devices NASNet is a CNN-based architecture discovered via Neural Architecture Search (NAS) that can classify images from the Imagenet dataset. This is based on the implementation of NASNet found [here](https://github.com/huggingface/pytorch-image-models/tree/main). This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/nasnet) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary). Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.cn) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device. ## Getting Started There are two ways to deploy this model on your device: ### Option 1: Download Pre-Exported Models Below are pre-exported model assets ready for deployment. | Runtime | Precision | Chipset | SDK Versions | Download | |---|---|---|---|---| | ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/nasnet/releases/v0.62.1/nasnet-onnx-float.zip) | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/nasnet/releases/v0.62.1/nasnet-qnn_dlc-float.zip) | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/nasnet/releases/v0.62.1/nasnet-tflite-float.zip) For more device-specific assets and performance metrics, visit **[NASNet on Qualcomm® AI Hub](https://aihub.qualcomm.cn/models/nasnet)**. ### Option 2: Export with Custom Configurations Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/nasnet) Python library to compile and export the model with your own: - Custom weights (e.g., fine-tuned checkpoints) - Custom input shapes - Target device and runtime configurations This option is ideal if you need to customize the model beyond the default configuration provided here. See our repository for [NASNet on GitHub](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/nasnet) for usage instructions. ## Model Details **Model Type:** Model_use_case.image_classification **Model Stats:** - Activations (M): 19.4 - GMACs: 5.9 - Input resolution: 224x224 - Model checkpoint: nasnetalarge.tf_in1k - Model size (float): 338 MB - Number of parameters: 88.7M ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | NASNet | ONNX | float | Snapdragon® X2 Elite | 9.67 ms | 1 - 1 MB | NPU | NASNet | ONNX | float | Snapdragon® X Elite | 18.957 ms | 189 - 189 MB | NPU | NASNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 14.205 ms | 0 - 572 MB | NPU | NASNet | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 53.926 ms | 2 - 520 MB | NPU | NASNet | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 30.173 ms | 1 - 6 MB | NPU | NASNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 18.922 ms | 0 - 193 MB | NPU | NASNet | ONNX | float | Qualcomm® QCS8450 | 53.926 ms | 2 - 520 MB | NPU | NASNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 27.14 ms | 1 - 5 MB | NPU | NASNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 18.957 ms | 189 - 189 MB | NPU | NASNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 11.69 ms | 1 - 406 MB | NPU | NASNet | ONNX | float | Snapdragon® 8 Elite Mobile | 11.69 ms | 1 - 406 MB | NPU | NASNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 9.493 ms | 1 - 418 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® X2 Elite | 8.835 ms | 1 - 1 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® X Elite | 16.779 ms | 1 - 1 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 12.318 ms | 0 - 513 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 48.508 ms | 0 - 474 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 27.631 ms | 1 - 5 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 16.25 ms | 1 - 4 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® SA8775P | 24.414 ms | 1 - 355 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® SA8650P | 24.414 ms | 1 - 355 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® SA8255P | 24.414 ms | 1 - 355 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® QCS8450 | 48.508 ms | 0 - 474 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 24.615 ms | 1 - 4 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 16.779 ms | 1 - 1 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 9.759 ms | 1 - 350 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® SA7255P | 89.205 ms | 2 - 350 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® SA8295P | 38.601 ms | 1 - 310 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 9.759 ms | 1 - 350 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.804 ms | 0 - 367 MB | NPU | NASNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 12.615 ms | 0 - 694 MB | NPU | NASNet | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 50.191 ms | 0 - 652 MB | NPU | NASNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 27.851 ms | 0 - 193 MB | NPU | NASNet | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 16.542 ms | 4 - 201 MB | NPU | NASNet | TFLITE | float | Qualcomm® SA8775P | 24.813 ms | 0 - 537 MB | NPU | NASNet | TFLITE | float | Qualcomm® SA8650P | 24.813 ms | 0 - 537 MB | NPU | NASNet | TFLITE | float | Qualcomm® SA8255P | 24.813 ms | 0 - 537 MB | NPU | NASNet | TFLITE | float | Qualcomm® QCS8450 | 50.191 ms | 0 - 652 MB | NPU | NASNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 24.558 ms | 0 - 192 MB | NPU | NASNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 10.486 ms | 0 - 534 MB | NPU | NASNet | TFLITE | float | Qualcomm® SA7255P | 89.677 ms | 0 - 538 MB | NPU | NASNet | TFLITE | float | Qualcomm® SA8295P | 40.111 ms | 0 - 486 MB | NPU | NASNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 10.486 ms | 0 - 534 MB | NPU | NASNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.248 ms | 0 - 549 MB | NPU ## License * The license for the original implementation of NASNet can be found [here](https://github.com/huggingface/pytorch-image-models?tab=Apache-2.0-1-ov-file). ## References * [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012) * [Source Model Implementation](https://github.com/huggingface/pytorch-image-models/tree/main) ## Community * For questions or feedback, chat with our engineers in [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.cn).